{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the hand gestures model with Turi Create\n",
    "\n",
    "On the small dataset this model gets 100% training accuracy, 80% test set accuracy (3 out of 15 images wrong)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import turicreate as tc\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('5.6', '70491')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tc.__version__, tc.version_info.build_number"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train_data = tc.image_analysis.load_images(\"../Dataset/train\", with_path=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"label\"] = train_data[\"path\"].apply(lambda path: os.path.basename(os.path.split(path)[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">path</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">image</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">../Dataset/train/✊/07E976<br>2F-F528-492A-B6BF-72E ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 224 Width: 224</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">✊</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">../Dataset/train/✊/2DBC44<br>03-2549-43BF-A3B4-871 ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 224 Width: 224</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">✊</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">../Dataset/train/✊/48D728<br>9A-4724-4AAB-9B53-E0C ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 224 Width: 224</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">✊</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">../Dataset/train/✊/4F54FB<br>78-EAA2-403A-84DF-356 ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 224 Width: 224</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">✊</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">../Dataset/train/✊/765B63<br>CA-F3B9-4C96-9A4B-095 ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 224 Width: 224</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">✊</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">../Dataset/train/✊/85D131<br>B4-B828-4724-9FB9-82A ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 224 Width: 224</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">✊</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">../Dataset/train/✊/8FDEEF<br>3B-8679-40A2-9671-51B ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 224 Width: 224</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">✊</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">../Dataset/train/✊/AF3D08<br>88-14E3-4717-B2DF-9D4 ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 224 Width: 224</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">✊</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">../Dataset/train/✊/E42439<br>A2-BF2D-4E36-8BEE-D96 ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 224 Width: 224</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">✊</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">../Dataset/train/✊/FF94A2<br>37-E25E-4D0D-9A5B-109 ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 224 Width: 224</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">✊</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[10 rows x 3 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tpath\tstr\n",
       "\timage\tImage\n",
       "\tlabel\tstr\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+-------------------------------+------------------------+-------+\n",
       "|              path             |         image          | label |\n",
       "+-------------------------------+------------------------+-------+\n",
       "| ../Dataset/train/✊/07E9762... | Height: 224 Width: 224 |   ✊   |\n",
       "| ../Dataset/train/✊/2DBC440... | Height: 224 Width: 224 |   ✊   |\n",
       "| ../Dataset/train/✊/48D7289... | Height: 224 Width: 224 |   ✊   |\n",
       "| ../Dataset/train/✊/4F54FB7... | Height: 224 Width: 224 |   ✊   |\n",
       "| ../Dataset/train/✊/765B63C... | Height: 224 Width: 224 |   ✊   |\n",
       "| ../Dataset/train/✊/85D131B... | Height: 224 Width: 224 |   ✊   |\n",
       "| ../Dataset/train/✊/8FDEEF3... | Height: 224 Width: 224 |   ✊   |\n",
       "| ../Dataset/train/✊/AF3D088... | Height: 224 Width: 224 |   ✊   |\n",
       "| ../Dataset/train/✊/E42439A... | Height: 224 Width: 224 |   ✊   |\n",
       "| ../Dataset/train/✊/FF94A23... | Height: 224 Width: 224 |   ✊   |\n",
       "+-------------------------------+------------------------+-------+\n",
       "[10 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "+------------------+-------+----------+\n",
       "|       item       | value | is exact |\n",
       "+------------------+-------+----------+\n",
       "|      Length      |   30  |   Yes    |\n",
       "| # Missing Values |   0   |   Yes    |\n",
       "| # unique values  |   3   |    No    |\n",
       "+------------------+-------+----------+\n",
       "\n",
       "Most frequent items:\n",
       "+-------+-------+\n",
       "| value | count |\n",
       "+-------+-------+\n",
       "|   ✊   |   10  |\n",
       "|   ✋   |   10  |\n",
       "|   ✌️  |   10  |\n",
       "+-------+-------+\n"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"label\"].summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = tc.image_analysis.load_images(\"../Dataset/test\", with_path=True)\n",
    "test_data[\"label\"] = test_data[\"path\"].apply(lambda path: os.path.basename(os.path.split(path)[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "+------------------+-------+----------+\n",
       "|       item       | value | is exact |\n",
       "+------------------+-------+----------+\n",
       "|      Length      |   15  |   Yes    |\n",
       "| # Missing Values |   0   |   Yes    |\n",
       "| # unique values  |   3   |    No    |\n",
       "+------------------+-------+----------+\n",
       "\n",
       "Most frequent items:\n",
       "+-------+-------+\n",
       "| value | count |\n",
       "+-------+-------+\n",
       "|   ✊   |   5   |\n",
       "|   ✋   |   5   |\n",
       "|   ✌️  |   5   |\n",
       "+-------+-------+\n"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"label\"].summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Materializing SFrame</pre>"
      ],
      "text/plain": [
       "Materializing SFrame"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore interactively -- this is only supported on macOS.\n",
    "train_data.explore()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at an image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Height: 224px\n",
       " Width: 224px\n",
       " Channels: 3,\n",
       " turicreate.data_structures.image.Image,\n",
       " numpy.ndarray,\n",
       " dtype('uint8'))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = train_data[7][\"image\"]\n",
    "img, type(img), type(img.pixel_data), img.pixel_data.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1240a2ef0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img.pixel_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_type = \"squeezenet_v1.1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Downloading base mlmodel</pre>"
      ],
      "text/plain": [
       "Downloading base mlmodel"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Downloading https://docs-assets.developer.apple.com/coreml/models/SqueezeNet.mlmodel to /var/folders/2h/jdd0ymmj2bgck8148622rkbr0000gn/T/model_cache/squeezenet_v1.1.mlmodel</pre>"
      ],
      "text/plain": [
       "Downloading https://docs-assets.developer.apple.com/coreml/models/SqueezeNet.mlmodel to /var/folders/2h/jdd0ymmj2bgck8148622rkbr0000gn/T/model_cache/squeezenet_v1.1.mlmodel"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Analyzing and extracting image features.</pre>"
      ],
      "text/plain": [
       "Analyzing and extracting image features."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+------------------+--------------+------------------+</pre>"
      ],
      "text/plain": [
       "+------------------+--------------+------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Images Processed | Elapsed Time | Percent Complete |</pre>"
      ],
      "text/plain": [
       "| Images Processed | Elapsed Time | Percent Complete |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+------------------+--------------+------------------+</pre>"
      ],
      "text/plain": [
       "+------------------+--------------+------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 30               | 988.812ms    | 100%             |</pre>"
      ],
      "text/plain": [
       "| 30               | 988.812ms    | 100%             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+------------------+--------------+------------------+</pre>"
      ],
      "text/plain": [
       "+------------------+--------------+------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Logistic regression:</pre>"
      ],
      "text/plain": [
       "Logistic regression:"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of examples          : 30</pre>"
      ],
      "text/plain": [
       "Number of examples          : 30"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of classes           : 3</pre>"
      ],
      "text/plain": [
       "Number of classes           : 3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of feature columns   : 1</pre>"
      ],
      "text/plain": [
       "Number of feature columns   : 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of unpacked features : 1000</pre>"
      ],
      "text/plain": [
       "Number of unpacked features : 1000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of coefficients      : 2002</pre>"
      ],
      "text/plain": [
       "Number of coefficients      : 2002"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting L-BFGS</pre>"
      ],
      "text/plain": [
       "Starting L-BFGS"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iteration | Passes   | Step size | Elapsed Time | Training Accuracy |</pre>"
      ],
      "text/plain": [
       "| Iteration | Passes   | Step size | Elapsed Time | Training Accuracy |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0         | 5        | 0.031155  | 0.048297     | 0.333333          |</pre>"
      ],
      "text/plain": [
       "| 0         | 5        | 0.031155  | 0.048297     | 0.333333          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1         | 9        | 0.654254  | 0.115815     | 0.500000          |</pre>"
      ],
      "text/plain": [
       "| 1         | 9        | 0.654254  | 0.115815     | 0.500000          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2         | 10       | 0.817817  | 0.133054     | 0.533333          |</pre>"
      ],
      "text/plain": [
       "| 2         | 10       | 0.817817  | 0.133054     | 0.533333          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3         | 11       | 1.000000  | 0.150392     | 0.466667          |</pre>"
      ],
      "text/plain": [
       "| 3         | 11       | 1.000000  | 0.150392     | 0.466667          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4         | 12       | 1.000000  | 0.167503     | 0.633333          |</pre>"
      ],
      "text/plain": [
       "| 4         | 12       | 1.000000  | 0.167503     | 0.633333          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 9         | 21       | 1.000000  | 0.286280     | 1.000000          |</pre>"
      ],
      "text/plain": [
       "| 9         | 21       | 1.000000  | 0.286280     | 1.000000          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 28        | 52       | 0.777266  | 0.705312     | 1.000000          |</pre>"
      ],
      "text/plain": [
       "| 28        | 52       | 0.777266  | 0.705312     | 1.000000          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.5 s, sys: 442 ms, total: 3.94 s\n",
      "Wall time: 5.71 s\n"
     ]
    }
   ],
   "source": [
    "%time model = tc.image_classifier.create(train_data, target=\"label\", model=model_type, \\\n",
    "                                         verbose=True, max_iterations=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class                                    : ImageClassifier\n",
      "\n",
      "Schema\n",
      "------\n",
      "Number of classes                        : 3\n",
      "Number of feature columns                : 1\n",
      "Input image shape                        : (3, 227, 227)\n",
      "\n",
      "Training summary\n",
      "----------------\n",
      "Number of examples                       : 30\n",
      "Training loss                            : 0.08\n",
      "Training time (sec)                      : 5.7119\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class                          : LogisticClassifier\n",
       "\n",
       "Schema\n",
       "------\n",
       "Number of coefficients         : 2002\n",
       "Number of examples             : 30\n",
       "Number of classes              : 3\n",
       "Number of feature columns      : 1\n",
       "Number of unpacked features    : 1000\n",
       "\n",
       "Hyperparameters\n",
       "---------------\n",
       "L1 penalty                     : 0.0\n",
       "L2 penalty                     : 0.01\n",
       "\n",
       "Training Summary\n",
       "----------------\n",
       "Solver                         : lbfgs\n",
       "Solver iterations              : 28\n",
       "Solver status                  : SUCCESS: Optimal solution found.\n",
       "Training time (sec)            : 0.4424\n",
       "\n",
       "Settings\n",
       "--------\n",
       "Log-likelihood                 : 0.08\n",
       "\n",
       "Highest Positive Coefficients\n",
       "-----------------------------\n",
       "__image_features__[625]        : 0.1048\n",
       "__image_features__[690]        : 0.0763\n",
       "__image_features__[814]        : 0.0754\n",
       "__image_features__[537]        : 0.063\n",
       "__image_features__[665]        : 0.0627\n",
       "\n",
       "Lowest Negative Coefficients\n",
       "----------------------------\n",
       "(intercept)                    : -0.1461\n",
       "__image_features__[547]        : -0.115\n",
       "__image_features__[734]        : -0.0697\n",
       "__image_features__[625]        : -0.0642\n",
       "__image_features__[346]        : -0.0634"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 227, 227)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.input_image_shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'squeezenet_v1.1'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['✊', '✋', '✌️']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Analyzing and extracting image features.</pre>"
      ],
      "text/plain": [
       "Analyzing and extracting image features."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+------------------+--------------+------------------+</pre>"
      ],
      "text/plain": [
       "+------------------+--------------+------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Images Processed | Elapsed Time | Percent Complete |</pre>"
      ],
      "text/plain": [
       "| Images Processed | Elapsed Time | Percent Complete |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+------------------+--------------+------------------+</pre>"
      ],
      "text/plain": [
       "+------------------+--------------+------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 30               | 129.124ms    | 100%             |</pre>"
      ],
      "text/plain": [
       "| 30               | 129.124ms    | 100%             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+------------------+--------------+------------------+</pre>"
      ],
      "text/plain": [
       "+------------------+--------------+------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics = model.evaluate(train_data)\n",
    "metrics[\"accuracy\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Analyzing and extracting image features.</pre>"
      ],
      "text/plain": [
       "Analyzing and extracting image features."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+------------------+--------------+------------------+</pre>"
      ],
      "text/plain": [
       "+------------------+--------------+------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Images Processed | Elapsed Time | Percent Complete |</pre>"
      ],
      "text/plain": [
       "| Images Processed | Elapsed Time | Percent Complete |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+------------------+--------------+------------------+</pre>"
      ],
      "text/plain": [
       "+------------------+--------------+------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 15               | 67.642ms     | 100%             |</pre>"
      ],
      "text/plain": [
       "| 15               | 67.642ms     | 100%             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+------------------+--------------+------------------+</pre>"
      ],
      "text/plain": [
       "+------------------+--------------+------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics = model.evaluate(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy \n",
      " 0.8\n",
      "f1_score \n",
      " 0.7712842712842712\n",
      "log_loss \n",
      " 2.168304249990035\n",
      "precision \n",
      " 0.8492063492063493\n",
      "recall \n",
      " 0.7999999999999999\n",
      "auc \n",
      " 0.8866666666666667\n",
      "roc_curve \n",
      " +-----------+-----+-----+---+----+-------+\n",
      "| threshold | fpr | tpr | p | n  | class |\n",
      "+-----------+-----+-----+---+----+-------+\n",
      "|    0.0    | 1.0 | 1.0 | 5 | 10 |   0   |\n",
      "|   1e-05   | 0.9 | 1.0 | 5 | 10 |   0   |\n",
      "|   2e-05   | 0.9 | 1.0 | 5 | 10 |   0   |\n",
      "|   3e-05   | 0.9 | 1.0 | 5 | 10 |   0   |\n",
      "|   4e-05   | 0.9 | 1.0 | 5 | 10 |   0   |\n",
      "|   5e-05   | 0.9 | 1.0 | 5 | 10 |   0   |\n",
      "|   6e-05   | 0.8 | 1.0 | 5 | 10 |   0   |\n",
      "|   7e-05   | 0.7 | 1.0 | 5 | 10 |   0   |\n",
      "|   8e-05   | 0.7 | 1.0 | 5 | 10 |   0   |\n",
      "|   9e-05   | 0.7 | 1.0 | 5 | 10 |   0   |\n",
      "+-----------+-----+-----+---+----+-------+\n",
      "[300003 rows x 6 columns]\n",
      "Note: Only the head of the SFrame is printed.\n",
      "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
      "confusion_matrix \n",
      " +--------------+-----------------+-------+\n",
      "| target_label | predicted_label | count |\n",
      "+--------------+-----------------+-------+\n",
      "|      ✌️      |        ✌️       |   2   |\n",
      "|      ✊       |        ✊        |   5   |\n",
      "|      ✋       |        ✋        |   5   |\n",
      "|      ✌️      |        ✋        |   2   |\n",
      "|      ✌️      |        ✊        |   1   |\n",
      "+--------------+-----------------+-------+\n",
      "[5 rows x 3 columns]\n",
      "\n",
      "num_test_examples \n",
      " 15\n",
      "num_classes \n",
      " 3\n",
      "num_features \n",
      " 1\n",
      "input_image_shape \n",
      " (3, 227, 227)\n",
      "num_examples \n",
      " 30\n",
      "training_loss \n",
      " 0.08003342747477915\n",
      "training_time \n",
      " 5.711936950683594\n",
      "model \n",
      " squeezenet_v1.1\n",
      "max_iterations \n",
      " 100\n",
      "model_name \n",
      " squeezenet_v1.1\n",
      "confidence_threshold \n",
      " 0.5\n",
      "hesitant_threshold \n",
      " 0.2\n",
      "confidence_metric_for_threshold \n",
      " relative_confidence\n",
      "conf_mat \n",
      " [{'target_label': '✊', 'predicted_label': '✊', 'prob_default': 0, 'count': 5, 'prob': 4.429907722005296, 'norm_prob': 0.8859815444010591}, {'target_label': '✊', 'predicted_label': '✋', 'prob_default': 0, 'count': 0, 'prob': 0.5136157548839033, 'norm_prob': 0.10272315097678067}, {'target_label': '✊', 'predicted_label': '✌️', 'prob_default': 0, 'count': 0, 'prob': 0.05647652311080091, 'norm_prob': 0.011295304622160182}, {'target_label': '✋', 'predicted_label': '✊', 'prob_default': 0, 'count': 0, 'prob': 0.01185706272975362, 'norm_prob': 0.002371412545950724}, {'target_label': '✋', 'predicted_label': '✋', 'prob_default': 0, 'count': 5, 'prob': 4.983456782103774, 'norm_prob': 0.9966913564207548}, {'target_label': '✋', 'predicted_label': '✌️', 'prob_default': 0, 'count': 0, 'prob': 0.004686155166472749, 'norm_prob': 0.0009372310332945498}, {'target_label': '✌️', 'predicted_label': '✊', 'prob_default': 0, 'count': 1, 'prob': 0.9286000195041627, 'norm_prob': 0.18572000390083254}, {'target_label': '✌️', 'predicted_label': '✋', 'prob_default': 0, 'count': 2, 'prob': 2.346630036724598, 'norm_prob': 0.4693260073449196}, {'target_label': '✌️', 'predicted_label': '✌️', 'prob_default': 0, 'count': 2, 'prob': 1.7247699437712394, 'norm_prob': 0.3449539887542479}]\n",
      "sorted_labels \n",
      " ['✊', '✋', '✌️']\n",
      "label_metrics \n",
      " [{'label': '✌️', 'count': 5, 'correct_count': 2, 'predicted_count': 2, 'recall': 0.4, 'precision': 1.0}, {'label': '✋', 'count': 5, 'correct_count': 5, 'predicted_count': 7, 'recall': 1.0, 'precision': 0.7142857142857143}, {'label': '✊', 'count': 5, 'correct_count': 5, 'predicted_count': 6, 'recall': 1.0, 'precision': 0.8333333333333334}]\n",
      "labels \n",
      " ['✊', '✋', '✌️']\n",
      "test_data \n",
      " +-------+-------------------------------+------------------------+--------------+\n",
      "| __idx |              path             |         image          | target_label |\n",
      "+-------+-------------------------------+------------------------+--------------+\n",
      "|   0   | ../Dataset/test/✊/BB551DC3... | Height: 224 Width: 224 |      ✊       |\n",
      "|   1   | ../Dataset/test/✊/CA7291E6... | Height: 224 Width: 224 |      ✊       |\n",
      "|   2   | ../Dataset/test/✊/D109269E... | Height: 224 Width: 224 |      ✊       |\n",
      "|   3   | ../Dataset/test/✊/E2062F5D... | Height: 224 Width: 224 |      ✊       |\n",
      "|   4   | ../Dataset/test/✊/ED30CAFB... | Height: 224 Width: 224 |      ✊       |\n",
      "|   5   | ../Dataset/test/✋/5C49CFF8... | Height: 224 Width: 224 |      ✋       |\n",
      "|   6   | ../Dataset/test/✋/69E216C3... | Height: 224 Width: 224 |      ✋       |\n",
      "|   7   | ../Dataset/test/✋/A19E5846... | Height: 224 Width: 224 |      ✋       |\n",
      "|   8   | ../Dataset/test/✋/D1E0FD26... | Height: 224 Width: 224 |      ✋       |\n",
      "|   9   | ../Dataset/test/✋/E2D5A663... | Height: 224 Width: 224 |      ✋       |\n",
      "+-------+-------------------------------+------------------------+--------------+\n",
      "+-------------------------------+-----------------+-----------------------+\n",
      "|             probs             | predicted_label |        entropy        |\n",
      "+-------------------------------+-----------------+-----------------------+\n",
      "| [0.9912848726817797, 0.008... |        ✊        |  0.04747447468461367  |\n",
      "| [0.5793412617054898, 0.420... |        ✊        |   0.6195708601464914  |\n",
      "| [0.9859986475844094, 0.012... |        ✊        |  0.07042739758385266  |\n",
      "| [0.9852000523353227, 0.014... |        ✊        |  0.07013973401535606  |\n",
      "| [0.8880828876982942, 0.057... |        ✊        |  0.38963459246454124  |\n",
      "| [0.004981039954131239, 0.9... |        ✋        |  0.029083738883288673 |\n",
      "| [0.0013919237840319676, 0.... |        ✋        |  0.016353134183947696 |\n",
      "| [6.022786959358761e-05, 0.... |        ✋        |   0.0228206722623549  |\n",
      "| [5.717838441243739e-05, 0.... |        ✋        | 0.0005632668514958519 |\n",
      "| [0.005366692737584389, 0.9... |        ✋        |  0.030414860982331187 |\n",
      "+-------------------------------+-----------------+-----------------------+\n",
      "+--------------------+------------------------+---------+\n",
      "|     confidence     |  relative_confidence   | correct |\n",
      "+--------------------+------------------------+---------+\n",
      "| 0.9912848726817797 | -0.007544598883973119  |    1    |\n",
      "| 0.5793412617054898 |  -0.4206295563768709   |    1    |\n",
      "| 0.9859986475844094 | -0.011922123251158965  |    1    |\n",
      "| 0.9852000523353227 | -0.014797577817096777  |    1    |\n",
      "| 0.8880828876982942 | -0.0022453754440025786 |    1    |\n",
      "| 0.9949661069793513 |  -0.9949132539128338   |    1    |\n",
      "| 0.9976780694870812 |  -0.9967480627581943   |    1    |\n",
      "| 0.9962371267419559 |  -0.9925344813535053   |    1    |\n",
      "| 0.9999426374323075 |  -0.9999424532490274   |    1    |\n",
      "| 0.9946328414630778 |  -0.9946323756637401   |    1    |\n",
      "+--------------------+------------------------+---------+\n",
      "[15 rows x 10 columns]\n",
      "Note: Only the head of the SFrame is printed.\n",
      "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
      "feature \n",
      " image\n"
     ]
    }
   ],
   "source": [
    "for k, v in metrics.items():\n",
    "    print(k, \"\\n\", v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">target_label</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">predicted_label</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">✌️</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">✌️</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">✊</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">✊</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">✋</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">✋</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">✌️</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">✋</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">✌️</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">✊</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[5 rows x 3 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\ttarget_label\tstr\n",
       "\tpredicted_label\tstr\n",
       "\tcount\tint\n",
       "\n",
       "Rows: 5\n",
       "\n",
       "Data:\n",
       "+--------------+-----------------+-------+\n",
       "| target_label | predicted_label | count |\n",
       "+--------------+-----------------+-------+\n",
       "|      ✌️      |        ✌️       |   2   |\n",
       "|      ✊       |        ✊        |   5   |\n",
       "|      ✋       |        ✋        |   5   |\n",
       "|      ✌️      |        ✋        |   2   |\n",
       "|      ✌️      |        ✊        |   1   |\n",
       "+--------------+-----------------+-------+\n",
       "[5 rows x 3 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics[\"confusion_matrix\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype: str\n",
       "Rows: 15\n",
       "['✊', '✊', '✊', '✊', '✊', '✋', '✋', '✋', '✋', '✋', '✌️', '✋', '✌️', '✊', '✋']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(test_data)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1242f6cc0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(test_data[1][\"image\"].pixel_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+--------------------+\n",
      "| class |    probability     |\n",
      "+-------+--------------------+\n",
      "|   ✊   | 0.9912848726817797 |\n",
      "|   ✊   | 0.5793412617054898 |\n",
      "|   ✊   | 0.9859986475844094 |\n",
      "|   ✊   | 0.9852000523353227 |\n",
      "|   ✊   | 0.8880828876982942 |\n",
      "|   ✋   | 0.9949661069793513 |\n",
      "|   ✋   | 0.9976780694870812 |\n",
      "|   ✋   | 0.9962371267419559 |\n",
      "|   ✋   | 0.9999426374323075 |\n",
      "|   ✋   | 0.9946328414630778 |\n",
      "|   ✌️  | 0.7053126350756357 |\n",
      "|   ✋   | 0.9920543309266389 |\n",
      "|   ✌️  | 0.9998941216256824 |\n",
      "|   ✊   | 0.8397451812432725 |\n",
      "|   ✋   | 0.9869981347775145 |\n",
      "+-------+--------------------+\n",
      "[15 rows x 2 columns]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "output = model.classify(test_data)\n",
    "output.print_rows(num_rows=15, num_columns=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Materializing SFrame</pre>"
      ],
      "text/plain": [
       "Materializing SFrame"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imgs_with_pred = test_data.add_columns(output)\n",
    "imgs_with_pred.explore()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export to Core ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading https://docs-assets.developer.apple.com/turicreate/models/squeezenet_v1.1-symbol.json\n",
      "Download completed: /var/folders/2h/jdd0ymmj2bgck8148622rkbr0000gn/T/model_cache/squeezenet_v1.1-symbol.json\n",
      "Downloading https://docs-assets.developer.apple.com/turicreate/models/squeezenet_v1.1-0000.params\n",
      "Download completed: /var/folders/2h/jdd0ymmj2bgck8148622rkbr0000gn/T/model_cache/squeezenet_v1.1-0000.params\n"
     ]
    }
   ],
   "source": [
    "model.export_coreml(\"HandsTuri.mlmodel\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
